Modified Hopfield neural network computational technique for real-time fusion of multimode radar/SAR imagery

We address a new approach to the problem of improvement of the quality of remote sensing (RS) imagery obtained with multimode imaging radar/SAR systems that employ different image formation methods via performing the collaborative RS image/method fusion. The collaborative considerations involve adaptive adjustment of the user-controllable regularization degrees of freedom in a particular image formation scheme. We develop the Hopfield neural network-adapted computational methodology for performing such data fusion employing the recently developed descriptive experiment design regularization (DEDR) framework aggregated with the variational analysis (VA) image enhanced approach. The addressed modified maximum entropy neural network (MENN) technique performs the collaborative reconstruction-fusion task in an efficient computational fashion ensuring on-line dynamic updates only of higher quality information from the input multimode image frames. The reported simulations verify that the developed DEDR-VA optimal MENN fusion technique outperforms the recently proposed iterative enhanced radar/SAR imaging methods both in the achievable resolution enhancement and the convergence rate.